AI0-001 Implementing AI Solutions Practice Question
An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune a pre-trained text classification model
Fine-tuning a pre-trained text classification model is a standard and effective approach for supervised classification when labeled data is available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a rule-based system with keyword matching
Why it's wrong here
Rule-based systems are brittle and cannot handle the variety of natural language.
- ✗
Use a prompt-based LLM with few-shot examples
Why it's wrong here
Prompt-based LLMs may be less accurate for classification and more expensive than a fine-tuned model.
- ✓
Fine-tune a pre-trained text classification model
Why this is correct
Fine-tuning leverages existing knowledge and works well with 10k labeled examples.
- ✗
Train a custom neural network from scratch
Why it's wrong here
Training a custom neural network from scratch requires tens of thousands of labelled examples per category to avoid overfitting, yet the dataset contains only 10,000 tickets total, which is insufficient for reliable generalisation. This approach is tempting because it offers full architectural control for highly specialised or novel classification tasks, and would be correct if the organisation possessed orders-of-magnitude more data and lacked a pre-trained language model.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.